Web search and content-based advertising are two important applications of the Internet. One important component of web search, and of some content-based advertising systems, is document ranking in which relevant documents, e.g. web documents or advertisements, are ranked with respect to a given query or content. Several advanced ranking techniques are in development to improve search result and advertising match accuracy.
During recent years, various search engines have been developed to facilitate searching information, products, services, and the like, over the world-wide web. One of the key components of a search engine, from a user experience perspective, is ranking the “relevant” documents that are displayed in response to a query specified by the user. Document ranking is done based on a multitude of metrics such as degree of query match and freshness of the document. One type of advanced document ranking incorporates implicit feedback and a large number of document features, which helps to improve the “quality” of the search results. This type of advanced document ranking, however, adversely affects the query processing time due to the CPU intensive nature of the document ranking process. Hence, in many cases, such advanced techniques are computationally intensive and thus cannot be deployed in production, which in turn limits the scope of improvements to search ranking and content-based advertising.